The present embodiments relate to medical imaging. In particular, automated identification of anatomical structure and/or control of medical imaging are provided.
One type of medical imaging is ultrasound. Echocardiography is ultrasound imaging of the human heart. Echocardiography is a commonly used imaging modality to visualize the structure of the heart. Because the echo is often a 2D projection of the 3D human heart, standard views are captured to better visualize the cardiac structures. For example, in the apical four-chamber (A4C) view, all four cavities, namely left and right ventricles, and left and right atria, are present. In the apical two-chamber (A2C) view, only the left ventricle and the left atrium are present.
Acquired cardiac views often deviate from the standard views due to machine properties, the inter-patient variations, or the skill or preference of sonographers. The sonographer manually adjusts imaging parameters of the ultrasound system and transducer position for echocardiography, resulting in variation. An imaging configuration may be selected from a list of organs (presets) with predetermined settings of imaging parameters. However, the presets require keystrokes and interruption of the workflow to invoke new settings each time the anatomical view changes. In order to keep the list manageable, the list may not contain all the variations and sub-cases. For example, in echocardiography, there are many views or imaging planes even though the same organ is imaged (heart). For each view, the optimal location of the color Doppler panbox is different.
After acquiring images based on the settings, the views may be classified automatically. However, detection of multiple objects is challenging. In U.S. Pat. No. 7,092,749, template modeling or matching is used to identify different structures. Trained classifiers have also been used. Most existing approaches train a separate binary classifier for each object against the background, and scan the input image for objects. Using different classifiers for different objects has inherent disadvantages. In training, the training complexity increases linearly with the number of classes. In testing, it is likely that several classifiers identify different structure at the same location. Due to the difficulty in comparing responses among these classifiers, determining the actual object needs additional work, such as training pairwise classifiers between two object classes. Also, evaluating multiple binary classifiers is a time-consuming process.